ASAIL2023: Automated Semantic Analysis of Information in Legal Text University of Minho Braga, Portugal, June 23, 2023 |
Conference website | https://sites.google.com/view/asail/asail-home |
Submission link | https://easychair.org/conferences/?conf=asail2023 |
Submission deadline | May 3, 2023 |
The Sixth Workshop on Automated Detection, Extraction and Analysis of Semantic Information in Legal Texts (ASAIL) will be held online in conjunction with the 19th International Conference on Artificial Intelligence and Law (ICAIL 2023). It is a continuation of the successful prior ASAIL workshops at ICAIL and JURIX.
This workshop will bring together an interdisciplinary group of scholars, academic and corporate researchers, legal practitioners, and legal service providers for an extended, collaborative discussion about the application of natural language processing, including the use of computational models and machine learning, to the semantic analysis of legal texts. Semantic analysis is the process of relating syntactic elements and structures, drawn from the levels of phrases, clauses, sentences, paragraphs, and whole documents, to their language-independent meanings in a given domain, including meanings specific to legal information. The range of focal texts includes:
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statutes, regulations, and court-made pronouncements of legal rules embodying legal norms;
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textual arguments in legal case decisions interpreting legal norms and applying them in concrete fact situations;
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legislative and policy-based debates concerning proposed legal norms, their purpose and meaning;
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actual and proposed contracts that need to be analyzed for the permissions and obligations they encode and their consistency with organisational preferences or legal frameworks;
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technical reports and other evidentiary documents;
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court testimony and narrative texts in submissions by self-represented parties.
Researchers have long been developing tools to aggregate, synthesise, structure, summarise, and reason about legal norms and arguments in texts. Current dramatic advances in natural language processing, text and argument mining, information extraction, and automated question answering are changing how automatic semantic analysis of legal rules and arguments will be performed in the future. In particular, the recent breakthrough in natural language processing brought about by neural network models, including transfer learning using complex language models, has created immense new potential for leveraging legal text for technology supporting legal practice, research, argumentation, and decision making. At the same time, increasing awareness of the mandate of ethical use of AI is fuelling a debate about the requirements of such systems and motivates important exploratory work on explainable and justifiable AI that is particularly crucial for the legal domain. The ASAIL workshop provides a forum for the proliferation of exciting ideas that advance the field of semantic analysis of legal texts.
Submission Guidelines
We invite papers written in English on, and demonstrations of, original work on the above listed and other aspects of automated detection, extraction and analysis of semantic information in legal texts. Three types of papers are solicited:
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full research papers (10 pages in the approved style plus bibliography);
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short papers (6 pages in the approved style plus bibliography); and
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position papers (2 pages in the approved style plus bibliography).
The main aim of the workshop is to elicit thoughtful discussion of novel ideas, to that end we particularly encourage submission of relevant short and position papers to the workshop. Position papers will be expected to outline novel research that is in its early stages without substantive results, whereas full and short papers will express a tangible contribution and will be evaluated accordingly.
To maintain ASAIL’s relevance in the larger rapidly-moving field of legal text analytics, paper submissions must explicitly identify their substantial (for full and short papers) or potential (for position papers) contribution to the state of the art and provide a satisfying amount of discussion apposite for the length of the paper. Possible forms of contribution include:
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Application of novel NLP techniques to a known corpus;
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Application of known NLP techniques to a novel corpus; and
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Detailed survey and analysis of a novel corpus that will be shared with the community and/or exhibits phenomena of broader interest.
For full and short papers only: In explaining a paper’s contribution, the authors should present, as well as discuss, their data, results and model behavior in sufficient depth, and go beyond reporting common metrics. Program committee members will be instructed to review submissions according to this standard.
ASAIL uses a single-blind peer-review process; authors are not required to anonymise any aspect of their submission, but reviewers will be kept anonymous to the authors. We adopt this process as an expedient balance between any concerns of bias and facilitating submission, with the expectation that bias is unlikely to adversely affect acceptance prospects for the workshop due to its scale.
While the bibliography is extraneous to the page limit, papers should be self-contained as ASAIL proceedings do not include appendices. A Program Committee will review all types of papers using the conference review system. Submissions will be evaluated on appropriateness for this call, originality of the research described and technical quality. Authors of selected papers will be invited to present the papers at the Workshop, with at least one author per accepted paper expected to register and attend in person.
Our expectation is that accepted papers will be published as part of the workshop proceedings at CEUR-WS, as in prior ASAIL workshops. Hence, all papers must follow the two-column CEUR-WS Layout [Latex, Word]. Papers not conforming to the style or exceeding the length limitation will be rejected without review. Papers must be submitted via the ASAIL 2023 Easychair system by the due date.
List of Topics
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Application of NLP to analyse arguments in legal texts: identification, annotation, and extraction of argument elements; relating arguments; and classifying arguments
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Automated or semi-automated approaches to extracting legal norms from legal texts
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Creation/evaluation of high quality annotated natural language legal corpora
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Automated semantic analysis of legal texts
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Development of computer-supported annotation environments for automated semantic analysis of legal texts
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Applications of machine learning to train automatic systems on tasks related to semantic analysis of legal texts, identifying legal norms, or extracting legal argumentation
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Summarization, visualization, and information retrieval for legal texts
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Argument mining of court cases, legislative records, legal policy debates and other legal documents
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Automated translations of legal text to formal or abstract representations that can be used for reasoning
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Applications of computational models of legal argumentation to guide interpretation of legal texts
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Application of linguistic theories of syntax, semantics, pragmatics, and discourse to legal texts
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Adaptation of NLP tools to the particularities of legal texts
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Implications of the above developments for law students and legal education
Committees
Organising Committee
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Francesca Lagioia, European University Institute and University of Bologna
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Jack Mumford, University of Liverpool (chair)
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Daphne Odekerken, Utrecht University
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Hannes Westermann, University of Montreal
Advisory Board
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Kevin D. Ashley, University of Pittsburgh
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Katie Atkinson, University of Liverpool
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Karl Branting, MITRE Corporation
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Enrico Francesconi, Italian National Research Council (IGSG-CNR) and European Parliament
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Matthias Grabmair, Technical University of Munich
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Jaromír Šavelka, Carnegie Mellon University
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Vern R. Walker, Maurice A. Deane School of Law at Hofstra University
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Bernhard Waltl, BMW Group AG
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Adam Wyner, Swansea University
Programme Committee
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Tommaso Agnoloni, Italian National Research Council (ITTIG-CNR)
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Elliott Ash, ETH Zurich
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Mi-Young Kim, University of Alberta
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Georg Rehm, DFKI
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David Restrepo Amariles, HEC Paris
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Marijn Schraagen, Utrecht University
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Francesca Toni, Imperial College London
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Giulia Venturi, Italian National Research Council (ILC-CNR)
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Raboud Winkels, University of Amsterdam
Contact
Jack Mumford: jack.mumford@liverpool.ac.uk